Abstract: Quantitative rating systems and techniques are described that prioritize customers by propensity to buy and buy size to generate customer ratings. In one example, a propensity model is used to determine a likelihood of a potential customer to purchase a product, and a projected timeframe buy size for the potential customer is determined. An expected value for the potential customer is generated by combining the likelihood of the potential customer to purchase the product and the projected timeframe buy size. In another example, a ratio model of annualized recurring revenue (ARR) is used to determine a timeframe buy size for an existing customer in consecutive time frames. An upsell opportunity for the existing customer is determined based on the timeframe buy size less an ARR for a current time frame for the existing customer. A rating of the potential or existing customer is output in a user interface.

Abstract: Quantitative rating systems and techniques are described that prioritize customers by propensity to buy and buy size to generate customer ratings. In one example, a propensity model is used to determine a likelihood of a potential customer to purchase a product, and a projected timeframe buy size for the potential customer is determined. An expected value for the potential customer is generated by combining the likelihood of the potential customer to purchase the product and the projected timeframe buy size. In another example, a ratio model of annualized recurring revenue (ARR) is used to determine a timeframe buy size for an existing customer in consecutive time frames. An upsell opportunity for the existing customer is determined based on the timeframe buy size less an ARR for a current time frame for the existing customer. A rating of the potential or existing customer is output in a user interface.

Abstract: Quantitative rating systems and techniques are described that prioritize customers by propensity to buy and buy size to generate customer ratings. In one example, a propensity model is used to determine a likelihood of a potential customer to purchase a product, and a projected timeframe buy size for the potential customer is determined. An expected value for the potential customer is generated by combining the likelihood of the potential customer to purchase the product and the projected timeframe buy size. In another example, a ratio model of annualized recurring revenue (ARR) is used to determine a timeframe buy size for an existing customer in consecutive time frames. An upsell opportunity for the existing customer is determined based on the timeframe buy size less an ARR for a current time frame for the existing customer. A rating of the potential or existing customer is output in a user interface.

Abstract: A document tracking method, a gateway device, and a server, where the gateway device intercepts a first data flow from a first terminal device in an internal network to a second terminal device in an external network, inserts a first Uniform Resource Locator (URL) into a first document in the first data flow to obtain a second data flow that includes a second document, and sends the second data flow to the second terminal device.

Abstract: Quantitative rating systems and techniques are described that prioritize customers by propensity to buy and buy size to generate customer ratings. In one example, a propensity model is used to determine a likelihood of a potential customer to purchase a product, and a projected timeframe buy size for the potential customer is determined. An expected value for the potential customer is generated by combining the likelihood of the potential customer to purchase the product and the projected timeframe buy size. In another example, a ratio model of annualized recurring revenue (ARR) is used to determine a timeframe buy size for an existing customer in consecutive time frames. An upsell opportunity for the existing customer is determined based on the timeframe buy size less an ARR for a current time frame for the existing customer. A rating of the potential or existing customer is output in a user interface.

Abstract: Prioritization techniques and systems are described that utilize a historical purchase sequence and customer features to prioritize products and services to generate product and service recommendations. In an example, feature data describing a customer and historical purchase data for the customer is received that indicates products or services purchased by the customer. The historical purchase data further includes indicators of when the products or services were purchased by the customer. Then, probabilities of future purchases by the customer of additional products are determined by classifying the additional products using a multiclass classification. The multiclass classification is based on the historical purchase data and the feature data describing the customer. Next, a ranking of the additional products is generated based on the determined probabilities of future purchases. The ranking of the additional products is output in a user interface based on the determined probabilities.

Abstract: A high voltage LDMOS device having high side source voltage, an n type buried layer and a p type buried layer situated on the interface between a p type substrate and an n type epitaxial layer; a lateral surface of the n type buried layer and a lateral surface of the p type buried layer not in contact, and are distant from one another with a distance, thereby increasing the withstand voltage between the n type buried layer and the p type buried layer; the p type buried layer and the drain overlap at least partially in a vertical direction, enabling the p type buried layer to exert a reduced surface field action on the drain, to increase the withstand voltage of the drain against the source; the source and the body terminal centrally on top of the n type buried layer.

Abstract: A high voltage LDMOS device having high side source voltage, an n type buried layer and a p type buried layer situated on the interface between a p type substrate and an n type epitaxial layer; a lateral surface of the n type buried layer and a lateral surface of the p type buried layer not in contact, and are distant from one another with a distance, thereby increasing the withstand voltage between the n type buried layer and the p type buried layer; the p type buried layer and the drain overlap at least partially in a vertical direction, enabling the p type buried layer to exert a reduced surface field action on the drain, to increase the withstand voltage of the drain against the source; the source and the body terminal centrally on top of the n type buried layer.